Energies 2011, 4, 2094-2114; doi:10.3390/en4112094
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energies
ISSN 1996-1073
www.mdpi.com/journal/energies
Article
The Impact of Carsharing on Public Transit and
Non-Motorized Travel: An Exploration of North American
Carsharing Survey Data
Elliot Martin * and Susan Shaheen
Transportation Sustainability Research Center (TSRC), University of California, Berkeley,
1401 S. 46th Street, Richmond, CA 94804, USA; E-Mail: [email protected]
* Author to whom correspondence should be addressed; E-Mail: [email protected];
Tel.: +1-510-665-3572; Fax: +1-510-665-2183.
Received: 20 August 2011; in revised form: 11 September 2011 / Accepted: 19 September 2011 /
Published: 24 November 2011
Abstract: By July 2011, North American carsharing had grown to an industry of nearly
640,000 members since its inception on the continent more than 15 years ago. Carsharing
engenders changes in member travel patterns both towards and away from public transit
and non-motorized modes. This study, which builds on the work of two previous studies,
evaluates this shift in travel based on a 6281 respondent survey completed in late-2008 by
members of major North American carsharing organizations. Across the entire sample, the
results showed an overall decline in public transit use that was statistically significant, as
589 carsharing members reduced rail use and 828 reduced bus use, while 494 increased rail
use and 732 increased bus use. Thus for every five members that use rail less, four
members use rail more, and for every 10 members that ride a bus less, almost nine
members ride the bus more. The people increasing and decreasing their transit use are
fundamentally different in terms of how carsharing impacts their travel environment. This
reduction, however, is also not uniform across all organizations; it is primarily driven by a
minority (three of eleven) of participating organizations. At the same time, members
exhibited a statistically significant increase in travel by walking, bicycling, and carpooling.
Across the sample, 756 members increased walking versus a 568 decrease, 628 increased
bicycling versus a 235 decrease, and 289 increased carpooling versus a decrease of
99 study participants. The authors found that 970 members reduced their auto commuting
to work, while 234 increased it. Interestingly, when these shifts are combined across
modes, more people increased their overall public transit and non-motorized modal use
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after joining carsharing than decreased it. Data collected on the commute distance of
respondents found that carsharing members tend to have shorter commutes than most
people living in the same zip code. The analysis also evaluates the distribution of
residential population density of members and its association with average changes in
driving. The analysis finds that average driving reductions are consistent across population
densities up to 10,000 persons/square kilometer but become more varied at higher densities.
Keywords: carsharing; public transit; walking; biking; commute distance
1. Introduction
The recent global economic decline has motivated individuals to seek more energy-efficient ways to
meet daily transportation needs. One alternative to the high fixed costs of vehicle ownership is
“carsharing”, which provides mobility similar to personal vehicle ownership. Members of carsharing
organizations pay for short-term access to a shared-vehicle fleet on a per-hour and per-mile/kilometer
basis. Carsharing vehicles are generally distributed throughout urban neighborhoods and city centers.
By providing temporary automotive access, carsharing services change the cost structure of private
vehicle use. Carsharing converts a large portion of private vehicle costs from fixed to variable. This
conversion allows people to use an automobile when they need it, paying as they go. In contrast to
fixed auto ownership costs, carsharing can be cost effective for people who can use alternative modes
to get to work and are not required to conduct routine long-distance driving. Previous research has
shown that people joining carsharing organizations reduce their personal driving and vehicle
ownership, which translates into reduced energy use [1–4]. Nevertheless, it is the personal monetary
savings achieved through carsharing that have largely contributed to its growth over the past decade.
As of July 2011, carsharing programs served nearly 640,000 individuals in metropolitan areas
throughout North America [5].
Carsharing facilitates both an increase and decrease in automobile use by urban residents. It
increases vehicle usage by allowing carless households to gain auto access, thus enabling them to drive
more. At the same time, carsharing also facilitates a decrease in auto use by allowing households that
own cars to alternatively obtain automobile access through shared vehicles. Such households can
decrease their automobile use and shift to public transit and non-motorized modes. This shift often
accompanies a reduction in travel and the number of vehicles owned by carsharing households, as
fewer personal vehicles are needed. For many individuals, carsharing can reduce—or even eliminate—
the need for private vehicle ownership. Research over the past decade has repeatedly evaluated the net
effect of carsharing on vehicle miles/kilometers traveled (VMT/VKT) and vehicle holdings, often
finding that it reduces both. In late-2008, the authors conducted a survey of nearly 6300 members of
11 carsharing organizations throughout North America. To date, this research has yielded two previous
evaluations. One evaluation reports on the net impact of carsharing on greenhouse gas (GHG)
emissions, and the other evaluates vehicle ownership changes among carsharing members [1,2]. This
paper is the third in this series and builds on the previous evaluations by exploring travel shifts with
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respect to walking, biking, public transit, and auto use in the commutes of carsharing members, as well
as patterns in commute distance and residential population density.
Broadly, researchers designed this survey to address fundamental questions related to how
carsharing impacts energy use, emissions, and vehicle ownership. To answer these questions, the
survey sought to evaluate travel behavior shifts. Furthermore, to provide context to those shifts,
researchers also evaluated the travel environment of members (e.g., location of home, work, etc.).
While the reduction in auto driving directly reduces energy use, understanding the nature of the modal
shift exhibited by carsharing members is relevant to explaining the transportation modes that support
reduced automobile dependency. For example, non-motorized travel mode shifts, such as walking and
biking, result in a near elimination of fossil energy use. For shifts to public transit and carpooling,
some increase in fossil energy use and emissions does occur. However, these increases as attributed to
a new rider are generally small, caused by the marginal energy required to move an additional
passenger in a vehicle that is otherwise making the trip anyway. There are several benefits that emerge
from developing a good understanding of these travel shifts and the travel environment of carsharing
members. Understanding the balance of this shift beyond the change in personal driving is important
because it defines how the carsharing population substitutes auto travel. This helps to establish the
relative importance of certain modes in substituting personal driving. At the same time, the survey data
can also serve to define the distribution of travel environment attributes of carsharing members,
including commute distance, time to work by mode, and the population density of member residences.
These attributes serve to inform the types of personal travel parameters that are most conducive to
carsharing use. Understanding both the travel shift and travel environment can support carsharing
networks and by extension the documented emission reductions from personal driving that result when
households join carsharing. While carsharing’s impact on driving and vehicle holdings has been the
subject of past research efforts, the details of the travel shift/environment among carsharing members
has been studied far less.
Building on these previous efforts, this paper has four main sections. First, the authors provide a
review of the relevant carsharing literature. Second, the survey design and analytical methodology are
discussed. Following the methodology, the authors present the results, which include an evaluation of
travel behavior changes, as well as some characterizations of the travel environment of members.
Finally, the paper concludes with a summary of key findings and recommendations for future study.
2. Literature Review
Previous research has found that carsharing has facilitated a considerable change in member
travel patterns. These impacts span a variety of metrics, including auto travel and vehicle ownership.
The authors’ recent study of carsharing members across North America estimated that carsharing
removed between 9 to 13 vehicles from the road for each shared vehicle deployed [2]. Previous North
American studies have yielded similar results but over a wider range⎯between 4.6 to 22.8 private
vehicles⎯depending on the region and study methodology [2–4,6–8]. The increasing prevalence of
carsharing—and the corresponding reduction in private vehicle ownership—can have implications on
how public transit is used in areas in which organizations operate. Carsharing has the potential to
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reduce VMT/VKT and promote alternative transportation modes such as public transit, walking, and
biking. But it can also lower the need of others to use these modes given greater automobile access.
Several European studies report notable reductions in private vehicle ownership, with 10 to 60% of
members selling a vehicle after joining a carsharing program. Carsharing was reported to enable
members to replace much of their driving with alternative modes such as public transit, walking, and
biking⎯resulting in annual VMT/VKT reductions of 30 to 70% [9–11]. According to a study by the
Swiss Federal Office of Energy, individuals shifted the majority of their motorized travel to public
transit after joining carsharing. In the absence of carsharing, these individuals would drive 26% more
and use public transit 12% less [12].
North American carsharing organizations have generally yielded results similar to the European
studies. Canadian studies have found that 15 to 29% of participants sold a vehicle after joining
carsharing [13,14], while US studies reported that 11 to 26% of carsharing members sold a vehicle
after joining [4,15,16]. Martin and Shaheen found that carsharing had a statistically significant impact
in reducing annual household GHG emissions. Households reduced their average annual GHGs by
0.58 metric tons of CO2-equivalents [1]. The “full” annual carsharing impact, which includes an
estimate of auto emissions from vehicles the household would have purchased in carsharing’s absence,
was found to be 0.84 metric tons of GHGs reduced per household per year [1]. In addition, the study
found that average observed VMT declined by 27% [1]. When the VMT of forgone vehicles “the full
impact” is considered, the decline in average VMT was found to be 43% [1].
Past research in the United States (U.S.) has yielded variable results, depending on the region and
time period under evaluation. The earliest carsharing organization in the U.S. was launched in San
Francisco in 1983. Walb and Loudon found with the early Short-Term Auto Rental (STAR) program
that most households reported either a decrease or no change in their public transit use. In addition,
STAR membership led to an increase in VMT/VKT from access to new vehicles [17]. In a first-year
study of the CarSharing Portland (CSP) program, Katzev discovered that, like STAR, CSP effectively
reduced members’ vehicle ownership. However, unlike STAR, CSP was reported to lead to an increase
in walking, biking, and public transit use [16]. Cooper et al. conducted a second-year CSP study, in
which they found that formerly carless members increased their VMT/VKT by 19%, while former
vehicle owners decreased their VMT/VKT by 25%, yielding an aggregate VMT/VKT reduction of 8%.
Members reported a 26% increase in walking, a 10% increase in biking, and a 14% increase in public
transit use [18].
Beginning in 2001, Cervero et al. conducted a series of three studies on the impacts of San
Francisco’s City CarShare program. The first study evaluated members after the first nine months of
operation and found that carsharing members averaged higher VMT/VKT than a control group,
suggesting some degree of travel inducement [19]. Cervero et al. attributed this to the fact that
two-thirds of the participants were from carless households, and many used carsharing as a substitute
for trips previously made using alternative modes. In a second-year follow-up study, Cervero and Tsai
found that participants exhibited slight VMT/VKT reductions, while the control group increased
VMT/VKT. Additionally, City CarShare membership made individuals more likely to use public
transit or non-motorized modes. However, other considerations—such as comparative travel time,
vehicle ownership, and transit pass availability—were more influential in determining travel mode [20].
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The final study in the series documented similar reductions in VMT/VKT during the third and fourth
program years [3].
Carsharing members tend to reside in medium- to high-density areas and use public transportation
frequently [8,19,21]. Celsor and Millard-Ball found that carsharing neighborhoods have vehicle
ownership rates that are lower, and one-person households are more common [22]. While
neighborhood density can be an important determinant of public transit use, it is less central to
carsharing success [22]. Stillwater et al. confirmed these results, finding that neither neighborhood
density or demographics played a large role in carsharing success (complementing the findings
presented later in this study) [23]. Stillwater et al. were also the first to examine the impact of street
width on carsharing, finding a significant negative correlation [23]. The observed relationship between
public transit availability and carsharing was ambiguous—light rail availability was positively related
to carsharing demand, while regional rail availability had a slightly negative effect on it. The authors
hypothesized that although carsharing and local transit are complementary, carsharing and regional
transit may act as substitutes [23].
Building on this growing body of literature, this study evaluates how public transit and non-motorized
travel shift among members of carsharing organizations in North America. In addition, the authors
explore the commuting needs of carsharing members based on the location of their home and work and
evaluate the distribution of annual VKT change by member population density.
3. Methodology
The survey respondents were recruited through 11 individual carsharing organizations, which directed
their members to take the survey through email solicitation. The respondents completed a 15-minute
questionnaire, which was designed to provide the data necessary for a “before-and-after” analysis. A
raffle incentive was offered to encourage survey response. This incentive was a $100 credit to the
respondent’s carsharing account. About 20 incentives were dispersed, with at least one member of each
organization receiving an incentive. The remaining incentives were drawn among the entire sample.
Respondents answered questions about their household’s travel behavior during the year before
they joined carsharing, including questions about annual VKT on private household vehicles,
carsharing vehicles, and travel via non-motorized modes and public transit. They were then asked to
evaluate the same annual parameters “at present.” Changes in net VKT and vehicles were used to
evaluate changes in emissions and vehicle holdings from carsharing [1,2].
Researchers collected two metrics of public transit activity shift from respondents. For the modes of
rail, bus, walking, bicycling, ferry, and carpool, respondents were asked to indicate the number of
round trips and hours that they traveled using each mode per week. These metrics were selected as to
allow respondents to express their travel activity changes in two separately measured but generalizable
ways. These metrics were requested for both the before-and-after periods of the survey to permit an
evaluation of travel change across public transit, non-motorized modes, and carpooling.
Respondents also were asked to provide information on the general location of their home and
work, in the form of two streets that intersected near their home along with a city. These data
preserved respondent privacy, while still providing an approximation of location superior to a zip code
centroid or a census tract.
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The zip code and postal code were used to link respondent locations with local demographics
derived from the latest government data. The zip code of each American respondent was linked to
2000 Census data, while the postal code of each Canadian respondent was linked to a 2006 Canadian
census tract, as defined by Statistics Canada.
The authors used information on respondent intersections for home and work locations to determine
the commute distance and time for each respondent. The intersection pair was employed to construct a
URL that was interpretable by Google™’s mapping interface. The URL was used to return directions
in a printable format to calculate an approximation of the driving time and distance of the respondent’s
commute. Directions for walking, biking, and public transit were also retrieved for each respondent to
evaluate how their driving times compared with travel times in alternative modes. Public transit data
were collected assuming travel at 8:00 AM and only consisted of time and not distance (which was not
reported). The 8:00 AM travel time was used to model the trip time by public transit during peak
service hours of the commute. Many public transit systems offer more frequent services during peak
hours, and thus the 8:00 AM assumption permitted the evaluation of transit travel time during periods
in which bus and train wait times would be less influential on overall travel time. This assumption does
not imply that travelers left for their commute at 8:00AM, but it does return close to the fastest
possible trip time by public transit. It is important to note that the directions are only an approximation
of the route that the respondent could take and not necessarily the route that the participant actually
takes (especially since the start and end locations are approximations). Nonetheless, the directions
serve as travel distance estimates that the respondent likely needs to traverse to get to work.
Furthermore, the incorporation of public transit directions permits the evaluation of the relative
differences in travel by public transit, biking, or walking, as compared to driving.
Beyond questions that evaluated travel patterns, the authors also included questions in the survey to
identify confounding factors that may affect the analysis; respondents with such factors were removed
accordingly. In total, 9635 individuals responded to the survey, and 6281 were included in the final
analysis. The most common cause of filtering was a change in residence or workplace that had a major
impact on travel patterns. The final dataset is the same used in the previous two studies [1,2]. A more
detailed description of the data processing methodology, including a discussion of the other causes of
filtering, can be found in Martin and Shaheen [1].
Although the survey was comprehensive in geographic scope, it did have limitations. One of the
major survey limitations was that it was a one-time inquiry of before-and-after travel patterns. This
required respondents to provide aggregate (annual) information of travel activity prior to carsharing.
This information is subject to some uncertainty and imprecision, but the key measurement in modal
shift is the direction of change as opposed to the magnitude of shift. Hence, the authors expected
respondents would have a general recollection as to whether they walked, biked and used public transit
more or less than they did before joining carsharing. For about two-thirds of respondents, their
membership duration was less than two years; thus, their recall time was not long.
An improved design would involve an intake survey of members upon entrance into the
organization and a follow-up survey a year after. Such a design would ultimately be implementable
only by carsharing organizations and was not feasible for this study.
The eleven participating carsharing organizations were: (1) Autoshare (Toronto, ON, Canada);
(2) City CarShare (San Francisco Bay Area, CA, U.S.); (3) CityWheels (Cleveland, OH, U.S.);
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(4) Community Car Share of Bellingham (Bellingham, WA, U.S.); (5) CommunAuto (Montreal,
Quebec City, Gatineau and Sherbrooke in Quebec, Canada); (6) Community Car (Madison, WI, U.S.);
(7) Co-operative Auto Network (Vancouver, BC, Canada); (8) IGo (Chicago, IL, U.S.);
(9) PhillyCarShare (Philadelphia, PA, U.S.); (10) VrtuCar Ottawa, ON, Canada); and (11) Zipcar (in
over 100 cities in the U.S. and Canada). The survey was launched in early-September 2008 and closed
on November 7, 2008. Most of the organizations, which are located in a single city, distributed e-mail
solicitations to all their members. Because of the size and geographic distribution of Zipcar, the sample
was bounded at 30,000 and directed at specific markets, consisting of 5000 each within New York
City, Boston, Washington, DC, Portland, and Seattle and 2500 each within Vancouver and Toronto.
Overall, the survey was estimated to reach about 100,000 carsharing members, with a response rate of
approximately 10%.
4. Results
The results suggest that carsharing has had a mixed impact on public transit and non-motorized
modal use. The impact on public transit appeared to be both positive and negative, with about the same
number of people increasing usage as decreasing it.
Table 1. Demographics of the North American Carsharing Member Sample.
Demographic Attribute
Gender
Male
Female
Age Category
30 or Younger
30 to 60
Older than 60
Education
Graduated High School
Some College or Associates Degree
Bachelor’s Degree
Graduate or Professional Degree
Other
Income (HH, $ US)
Less than $50,000
$50,000–$100,000
$100,000–$150,000
More than $150,000
Decline to Respond
United States
Carsharing
N = 4229
Canadian
Carsharing
N = 2024
Total Final
Total Complete
N = 6253
N = 9578
44%
56%
46%
54%
45%
55%
43%
57%
N = 4201
N = 1996
N = 6197
N = 9482
38%
57%
6%
31%
64%
5%
35%
59%
6%
40%
55%
5%
N = 4235
N = 2028
N = 6263
N = 9591
2%
13%
43%
41%
1%
4%
21%
39%
32%
3%
3%
16%
42%
38%
2%
2%
16%
42%
38%
2%
N = 4247
N = 2034
N = 6281
N = 9536
34%
34%
13%
10%
9%
33%
40%
12%
4%
10%
34%
36%
13%
8%
9%
36%
34%
13%
7%
10%
The impact on non-motorized modes and carpooling was found to be definitively positive, with
more members increasing walking, biking, and carpooling than decreasing. To understand the context
of these results, it is relevant to understand the demographics of the population, which are summarized
in Table 1. As is evident in this table, carsharing has served a population with diverse demographic
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characteristics. The gender split tilted slightly towards females, while the age distribution suggests that
carsharing has mostly appealed to younger individuals. The education level of carsharing members
was found to be high, relative to the U.S. as a whole. In 2008, about 28% of all U.S. citizens had a
bachelor’s degree, whereas more than 80% of carsharing members had a bachelor’s degree or
higher [24]. Finally, the income level of members suggests that although carsharing has mostly served
the middle class, more than 20% of the population had an income of $100,000 or higher.
The carsharing population has been predominantly comprised of households that were carless when
they joined. Of the final sample of 6281 households, 3885 (62%) entered carsharing as carless
households. Another 1917 (31%) entered carsharing with just one vehicle. Hence, more than 90% of
all households joining carsharing had no more than one automobile.
For most respondents, data of the two metrics reported (average weekly hours and average weekly
round trips) were highly correlated in magnitude and change. Both metrics provided a general
assessment as to the direction of change of non-motorized or public transit use. One caveat to this
question related to how it was asked to Canadian respondents. Feedback during the survey design
phase informed the authors that Canadian commute patterns change seasonally, as Canada has more
extreme winters than the U.S. Hence, asking how Canadians travel during a typical week is a
seasonally-sensitive question in many parts of the country. To avoid confusion, Canadians were asked
this question in the context of their travel during the month of April, which was chosen because it
precedes summer, but it is generally warm enough for non-motorized modes to be used in carsharing
cities. To illustrate the aggregate shift to and from non-motorized and public transit modes, Table 2
shows how respondents increased, decreased, and did not change their travel by each mode for both of
the measurements. The table shows the discrete count of respondents in each respective category as
well as the percent of the complete sample of 6281 respondents. For each shift by travel mode and
measure, the statistical significance of the shift is defined by the Wilcoxon Sign Rank Test.
Table 2. Aggregate Shift in Public Transit and Non-Motorized Modes.
Average Hours per Week
Mode
Rail
Bus
Walk
Bike
Carpool
Ferry
†
Round Trips per Week
Decreased
No
Change
Increased
Wilcoxon Sign
Rank Test
(P-value)
Decreased
No
Change
Increased
Wilcoxon Sign
Rank Test
(P-value)
589 (9%)
828 (13%)
568 (9%)
235 (4%)
99 (2%)
13 (0%)
5198
4721
4957
5418
5893
6262
494 (8%)
732 (12%)
756 (12%)
628 (10%)
289 (5%)
6 (0%)
0.001946 †
0.007537 †
1.19 × 10−7 *
<2.20 × 10−16 *
<2.20 × 10−16 *
0.05415
571 (9%)
783 (12%)
559 (9%)
219 (3%)
86 (1%)
14 (0%)
5226
4794
5046
5480
5932
6259
484 (8%)
704 (11%)
676 (11%)
582 (9%)
263 (4%)
8 (0%)
0.007395 †
0.02025 ‡
4.35 × 10−4 *
<2.20 × 10−16 *
<2.20 × 10−16 *
0.1004
One-tailed Wilcoxon Signed Rank Test, Decline Statistically Significant at 99%;
‡
One-tailed Wilcoxon Signed Rank
Test, Decline Statistically Significant at 95%; * One-tailed Wilcoxon Signed Rank Test, Increase Statistically Significant
at 99%.
Table 2 offers an initial perspective of the overall changes in public transit and non-motorized
travel among carsharing members. Carsharing has had a rather heterogeneous impact on public transit
and non-motorized travel. With respect to rail and bus, the overall travel change indicated that the
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number of members lowering their public transit use slightly exceeded the number of members
increasing usage. Given the sample size, this shift downwards was statistically significant at the 99%
confidence level. In contrast, a statistically significant shift upwards was observed in walking and
bicycling. The shift towards bicycling was the largest in overall magnitude, with nearly three times as
many members increasing their bicycling over those decreasing. A similar ratio was found for
carpooling, which was defined as general ridesharing, not just in commuting. The change in ferry use
was small and not statistically significant.
The comparatively large number of respondents lowering their public transit use after joining
carsharing could be driven by the large share of members from carless households. For these
households, automobile travel was not a readily available option prior to membership, and the
introduction of carsharing offered a new mode to conduct trips, which would have been previously
taken by public transit. While these results suggest that more carsharing members moved away from
public transit, it is important to note that the net difference between those increasing and decreasing
transit use was not large. These data suggest that for every five members that used rail less, four
members used rail more, and for every 10 members that rode a bus less, almost nine members rode the
bus more.
While Table 2 indicates that the overall impact of carsharing on public transit was slightly negative
on ridership, the magnitude and direction of these observed shifts were not universal across
organizations. When bus and rail shifts are listed by organization, the impact is found to be more
heterogeneous. Some organizations still had more members that reduced ridership than increased it,
while other organizations had a statistically insignificant net shift or a slight increase in ridership.
Table 3 illustrates the bus and rail shifts exhibited for the seven largest organizations in North
America. The sample size of each organization, presented in Table 3, is larger than 100. Organization
names are not listed due to proprietary reasons. The percentage (top number) within each box indicates
the respondent percentage within each organization, which either decreased, did not change, or
increased bus or rail use. The number in parenthetical (on the right) next to each percentage is the
average change in annual VKT reported by the shifting cohort within the organization. There are
several key points that emerge from Table 3. The subdivision of modal shift by organization shows
that most organizations exhibited relatively similar magnitudes of positive and negative shifts in rail
and bus transit. Furthermore, the shifts within most organizations were not large enough in any one
direction to be statistically significant. Rather, it was a minority of organizations that drove the overall
impact for both rail and bus transit. It is also important to note that those respondents increasing and
decreasing their bus and rail use reflected fundamentally different travel circumstances. Those that
were increasing their ridership were adapting to carsharing from a lifestyle of previous personal
automotive access, while those that were decreasing their ridership were adapting to carsharing from a
carless lifestyle. This distinction is highlighted by differences in the average net change in household
VKT of the corresponding cohort. For most cohorts increasing their public transit usage, the average
change was decidedly negative in nearly every case.
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Table 3. Shift in Public Transit Use by Seven Largest North American Carsharing
Organizations.
Average Hours per Week
Mode
RAIL
Organization
Percent
Decreased
Percent
No Change
Percent
Increased
1
8% (−15)
85% (−964)
2
10% (−3197)
3
P-value of
Mode Decline
P-value of Mode
Increase
6% (−8390)
0.01 ‡
0.99
79% (−2851)
11% (−7810)
0.72
0.28
15% (−2535)
72% (−1023)
12% (−11,407)
0.11
0.89
4
9% (−1341)
81% (−1540)
10% (−10,352)
0.83
0.17
5
11% (180)
77% (−1103)
11% (−8695)
0.5
0.5
6
BUS
Wilcoxon Sign Rank Test
4% (−6801)
0
†
12% (1540)
84% (−27)
7
0%
97% (−1816)
3% (3985)
*
*
1
9% (227)
81% (−793)
10% (−7663)
0.63
0.37
2
17% (−1403)
68% (−2569)
15% (−9808)
0.2
0.8
3
13% (−2079)
73% (−1312)
13% (−9717)
0.53
0.47
4
10% (−727)
80% (−1796)
10% (−8873)
0.61
0.39
5
11% (−485)
78% (−787)
11% (−10,143)
0.58
0.42
†
6
21% (2156)
68% (246)
11% (−6739)
0
7
25% (−323)
52% (646)
23% (−8269)
0.34
1
1
0.66
Round Trips per Week
Mode
RAIL
BUS
†
Organization
Percent
Decreased
Percent
No Change
Percent
Increased
1
8% (336)
86% (−1028)
2
10% (−3317)
3
Wilcoxon Sign Rank Test
P-value of
Mode Decline
P-value of Mode
Increase
6% (−8104)
0.02 ‡
0.98
79% (−2777)
11% (−8241)
0.72
0.28
15% (−1993)
72% (−1068)
13% (−11,379)
0.14
0.86
4
9% (−1518)
81% (−1600)
10% (−9873)
0.76
0.24
5
12% (835)
78% (−1268)
10% (−9693)
0.27
0.73
6
10% (523)
85% (146)
5% (−7069)
0†
1
7
0%
98% (−1512)
2% (−7360)
*
*
1
10% (−252)
81% (−765)
9% (−7895)
0.19
0.81
2
16% (−1241)
68% (−2743)
15% (−8728)
0.43
0.58
3
12% (−1796)
74% (−1285)
13% (−10,172)
0.68
0.32
4
10% (−1189)
80% (−1704)
10% (−8963)
0.72
0.28
5
11% (−445)
77% (−736)
12% (−9834)
0.65
0.35
†
6
17% (2234)
73% (227)
10% (−7132)
0
7
24% (−477)
55% (550)
21% (−8622)
0.29
1
0.71
One-tailed Wilcoxon Signed Rank Test, Decline Statistically Significant at 99%; * Change not large enough to conduct
test. Number in parenthetical is the average VKT change of cohort.
One exception did exist for an organization with a small sample size (n = 115) in its shift towards
higher rail. In comparison, the average decline in household VKT among those that decreased their
public transit use was far less, as this cohort constituted a high share of people that increased driving.
This large and consistent difference in average VKT change suggests that those increasing and
Energies 2011, 4
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decreasing their public transit use were not randomly distributed, but rather a function of the prior
circumstances under which they joined.
Overall, Tables 2 and 3 show that carsharing has had a mixed overall impact on public transit, while
definitively increasing the number of people walking, bicycling, and carpooling/ridesharing. However,
it is important to recognize that these shifts often did not occur only along a single mode. Many
members simultaneously combined their carsharing membership with shifts to other modes. To
provide insight into the coupling of modal shifts, Table 4 presents a cross-tabulation that shows how
members within the sample increased or decreased average hours of use of certain modes in
combination with others. For example, the upper left quadrant provides a count of members that
increased modes in combination with other modes. In this example, 212 respondents increased their
use of rail and bus, while in the upper-right quadrant, 88 respondents increased bus use but decreased
walking. Respondents could have indicated shifts in more than two modes, as a person could increase
rail, walking, and bicycling travel. In Table 4 below, this respondent is counted in the rail-walk cell,
the rail-bicycling cell, and the walk-bicycling cell all in the upper-left quadrant. Blank cells constitute
duplicative information. The results of this table show that a fair number of respondents also exhibited
shifts in both directions, reducing their use of one mode and increasing their use of another, as
indicated in the upper-right quadrant.
Table 4. Cross-Tabulation of Modal Shift Combinations.
Increased
Carpool
Ferry
Ferry
Biking
Carpool
Walk
Biking
Decreased
Walk
Bus
Bus
Rail
Rail
Ferry
Ferry
Carpool
Carpool
Biking
Biking
Walk
Walk
Increased
Bus
Bus
Rail
Rail
Decreased
494
212
210
124
44
2
0
60
69
25
19
3
732
338
194
69
2
80
0
88
48
29
1
756
215
55
2
99
122
0
45
23
2
628
42
2
107
142
122
0
29
2
289
1
45
74
54
27
0
0
6
1
2
0
1
0
0
589
203
143
46
13
4
828
201
70
23
4
568
65
24
6
235
4
1
99
1
13
While this analysis illustrates a neutral to negative shift away from public transit, the overall shift
towards public transit and non-motorized modes (e.g., walking and cycling)⎯when considered
together⎯was positive. This point is illustrated in Table 5a, which distributes respondents into four
categories based on how they shifted their overall public transit and non-motorized travel. Respondents
could: (1) increase only; (2) increase and decrease; (3) decrease only; or (4) exhibit no major change in
non-motorized and public transit travel. This form of categorization placed each respondent (n = 6281)
Energies 2011, 4
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within one category only. Table 5b shows the results of a different question that focuses on the shift in
automotive commuting among carsharing respondents. Respondents were asked: “During a typical
work week, how many days a week would you commute to work or school by car before joining
carsharing?” and “How many days a week do you commute by car currently?” to report changes in
auto commuting days.
Table 5. Change in Non-Motorized/Public Transit Travel and Auto Commuting.
(a) Overall Count of Respondents Shifting Non-motorized and Public Transit Travel
Average Observed
Respondent
Measurement
Direction of Change
VKT Change
Count
(VKT/year/hh)
Increased Public Transit and
1046 (17%)
−8070
Non-Motorized Travel Only
Both Increased and Decreased Public
755 (12%)
−2753
Transit and Non-Motorized Travel
Average Hours
Using Transit
Decreased Public Transit and
901 (14%)
705
Non-Motorized Travel Only
No Stated Change in Public Transit
3579 (57%)
−291
and Non-Motorized Travel
(b) Change in Days per Week of Commuting to Work or School by Personal Automobile
Average Observed
Respondent
Measurement
Direction of Change
VKT Change
Count
(VKT/year/hh)
Decreased
970 (15%) †
−8590
Days Commuting
No Stated Change
5077 (81%)
−612
by Automobile
Increased
234 (4%)
2188
†
One-tailed Wilcoxon Signed Rank Test, Decline Statistically Significant at 99%.
As the authors described in the methodology, the approximate home and work location of
respondents were used to generate an estimate of the respondent’s commute. Not all respondents
provided enough information to complete the commute computation; hence, the sample size of
estimated commutes was roughly two-thirds of the final sample. Figure 1 shows the distribution of
distance to work among respondents with valid commute calculations by four regions: the American
East, American West, Canadian East, and Canadian West. The division between West and East is
defined by cities east and west of the Mississippi river. For each respondent, the distance for both
walking and driving was computed. The two distances provide insight regarding the degree to which
walking and biking have advantages at shorter distances. A notable attribute of the distributions was
the large share of people who have a “zero” distance to work in each region. Those with a “zero”
commute distance included people who work at home, were retired, or otherwise did not have an
outside work location. Respondents were able to indicate whether one of those situations applied to
them in the survey. The share of respondents with a zero commute distance was notably higher in
Canada than in the U.S. In the U.S., both eastern and western cities had roughly the same (~11%) share
of respondents with zero commute distance. In Canada, the eastern share was 15%, whereas the
western share was nearly 25%.
Energies 2011, 4
2106
Figure 1. Distribution of Commute Distance of Carsharing Members by Region.
Canadian East
Respondents
American East
30%
30%
25%
25%
20%
20%
15%
N = 2186
Driving Distance
15%
10%
10%
5%
5%
0%
0%
American West
Respondents
Walking/Bicycling Distance
N = 1011
Canadian West
30%
30%
25%
25%
20%
20%
15%
15%
N = 710
10%
5%
5%
0%
0%
Kilometers to Work
N = 250
10%
Kilometers to Work
In Figure 1, the distribution of commute distances is divided into four regions to illustrate
differences and similarities. Across the entire sample, nearly 35% of respondents were within two
kilometers of walking/biking distance to work. Even more striking is that about 85% of the samples
were within 10 kilometers of walking/biking distance to work. While the shapes of the distributions
differ slightly by region⎯most notably the Canadian west, the distributions as divided by region
suggest that short commutes have been a common attribute of carsharing members across the continent.
Given the relative homogeneity of observed commute distances, it is natural to conclude that the
shape is at least in part derived from the concentration of carsharing networks in urban cores, where
distance to work is arguably shorter than suburban or rural regions. However, further analysis indicates
that the distribution of commute times for carsharing members was short even for people within their
own neighborhoods. To illustrate this, the authors collected demographic information on travel time to
work by zipcode, which is reported by the 2000 US Census [25]. The Census data include a distribution
of travel time to work among respondents in each zipcode. For all respondent zipcodes in the sample, the
authors evaluated the median travel time to work of each distribution. This permits a comparison of the
estimated travel-time-to-work of carsharing members with the median travel-time-to-work of other
residents within their respective zipcodes. This comparison is made with the two graphs in Figure 2.
Energies 2011, 4
2107
Figure 2. Comparison of Travel Time to Work of North American Carsharing Members
and the U.S. Population.
Carsharing Population Drive Time to Work
30%
28%
25%
20%
23%
Drive Time to Work, N = 3429
18%
17%
15%
10%
7%
3%
5%
2%
1%
1%
0%
0%
0%
1%
0%
Median Travel Time to Work within Respondent Zipcode (U.S.)
40%
35%
35%
30%
Median Travel Time to Work with Zipcode(U.S.), N = 2899
26%
25%
21%
20%
15%
15%
10%
5%
0%
0%
0%
0%
0%
1%
0%
0%
2%
0%
The top portion of Figure 2 illustrates the distribution of driving time to work for the carsharing
sample. The distribution consists of both American and Canadian respondents, but the distribution of
the two nationalities taken separately are very similar. The bottom graph shows the median travel time
within the zipcodes of American respondents (similar data were not collected in the Canadian census).
The two distributions provide a clear distinction between travel time for carsharing members and the
general population living in the same zip code. Households that lived closer to their work location or
work from home predominantly used carsharing.
For all respondents, driving was computed as the fastest mode to work. Nevertheless, it is
interesting to contrast the travel times of carsharing members across other modes. Figure 3 illustrates
the distribution of walking, bicycling, and public transit time to work for all respondents. The sample
sizes differed, as not all travel times of non-driving modes were computable.
The three distributions offer perspective on the commute times of carsharing members for three
non-automotive modes. Nearly 50% of the sample lived within a 10-minute bicycle ride of their
workplace. For short distances, bicycling performed better than public transit. This result reflected a
wait time based on actual public transit schedules in Google Maps™. This fixed time public transit
cost, coupled with respondents living closer to work, may have been a contributing factor in the higher
positive shift towards bicycle use⎯in contrast to public transit⎯among carsharing members.
Energies 2011, 4
2108
Figure 3. Distribution of Walking, Biking, and Public Transit Time to Work for North
American Carsharing Members Surveyed.
16%
14%
12%
10%
8%
6%
4%
2%
0%
25%
15%
14%
Walking Time to Work
N = 3,588
4%
5%
6%
5%
5%
4%
3%
4%
4%
21%
3%
3%
3%
3%
2%
2%
2%
2%
2%
1%
1%
1%
1%
1%
Bicycling Time to Work
20%
14% 13%
15%
4%
10%
N = 2,635
11%
9%
6%
5%
4%
4%
3%
3%
2%
1%
1%
1%
1%
3%
1%
1%
0%
0%
1%
0%
0%
0%
0%
0%
0%
20%
18%
16%
14%
12%
10%
8%
6%
4%
2%
0%
17%
Public Transit Time to Work
11% 11%
10% 11%
5%
N = 3,152
9%
6%
6%
4%
2%
2%
1%
1%
1%
1%
1%
0%
0%
0%
0%
0%
0%
0%
0%
0%
Minutes
The change in household vehicles before-and-after carsharing could have also influenced who
among the sample shifted towards public transit and non-motorized modes. To explore these
influences, the authors compute in Table 6 the average minutes to work for each mode among those
that increased, decreased, and did not change their use of public transit and non-motorized modes. For
the same subsets, the average change in vehicles is computed and presented alongside the average
travel times. This analysis explores whether those that increased the use of a mode did so because the
travel time with that mode was exceptionally shorter than those that decreased use of that mode.
The comparison suggests that a lower overall travel time was not a driving factor in determining
who shifted towards public transit and non-motorized modes as in most cases, the average travel time
of respondents that increased their use of these modes was generally higher than the average travel
time of those decreasing their use of these modes. However, changes in vehicle holdings are a more
significant factor. In almost every case, the average number of vehicles shed by respondents increasing
their use of these modes was two to four times higher than the average of those decreasing their public
transit and non-motorized mode use.
Energies 2011, 4
2109
Table 6. Subsample Averages of North American Carsharing Members Surveyed for Time
to Work and Change in Average Number of Household Vehicles.
Mode
Average Hours
Rail (Transit)
Bus (Transit)
Walk (Walk)
Bicycle (Bicycle)
Carpool (Drive)
Ferry (Transit)
Round Trips
Rail (Transit)
Bus (Transit)
Walk (Walk)
Bicycle (Bicycle)
Carpool (Drive)
Ferry (Transit)
Commuting by
Automobile
Days per Week
Commuting
Decreased
25
23
54
2
10
8
Decreased
25
23
53
3
9
6
No Change
23
23
59
3
8
23
No Change
23
23
58
3
8
23
Increased
28
25
53
3
9
18
Increased
28
25
59
3
10
18
Average Change in Household
Vehicles (Vehicles/Household)
Decreased No Change
Increased
−0.19
−0.21
−0.71
−0.16
−0.20
−0.69
−0.19
−0.19
−0.68
−0.14
−0.23
−0.59
−0.44
−0.24
−0.26
−0.22
−0.25
−1.25
Decreased No Change
Increased
−0.18
−0.21
−0.71
−0.16
−0.19
−0.69
−0.18
−0.20
−0.66
−0.14
−0.23
−0.59
−0.40
−0.24
−0.29
−0.33
−0.25
−1.00
Decreased
No Change
Increased
Decreased
No Change
Increased
11
9
13
−0.65
−0.16
0.03
Average Minutes to Work
Finally, data collected on population as well as zip and postal code size permitted the authors to
analyze the average VKT changes as correlated with population density. Figure 4 presents the
distribution of change in VKT of members that lived within a zip or postal code that had a population
density as defined on the horizontal axis. The bar graph distinguishes the American and Canadian
changes in VKT, and the plotted line illustrates the sample size by nationality and by density.
For both the American and Canadian respondents, the average change was negative at densities up
to 10,000 people per square kilometer. The solid gray line on the top half of the graph indicates the
total number of respondents that resided within a zip or postal code of the given density. As the
population density approached 15,000 people per square kilometer, the sample size dropped
precipitously for both Canadians and Americans. There were only 13 Canadian observations above
15,000 people per square kilometer, while over the same range there were 293 American observations.
Overall, the Canadian average driving change within this range was slightly positive, whereas the
American average was slightly negative. The intervals within these ranges are shown to illustrate the
variability across this region, with the caveat that sample sizes were too small to draw major inferences
of the impacts within higher densities. Thus, at densities up to 10,000 people per square kilometer, the
average change in VKT in both countries was almost uniformly negative. The limited sample size
leaves an open question as to the relationship between carsharing members in higher urban densities
and overall VKT changes.
Energies 2011, 4
2110
Figure 4. Average VKT Change by Population Density of Respondent.
20000
800
15000
600
500
10000
400
300
5000
200
100
0
0
‐100
‐5000
‐200
Respondents (Line)
Average Change in VKT per year per household (Bar chart)
700
‐300
‐10000
‐400
American, N = 4074
Canadian, N = 1907
Total Respondents
‐15000
‐500
‐600
American Respondents
Canadian Respondents
‐20000
‐700
‐800
Population per Sq. Kilometer
5. Conclusion
This study completes the third in a series on carsharing impacts in North America, based on a
survey conducted in Fall 2008. The results build on the insights drawn from the previous two studies
and offer perspective on how public transit and non-motorized modes may change with carsharing. As
reported in Martin and Shaheen and Martin et al., carsharing lowered overall GHG emissions and
vehicle holdings [1,2]. These data suggested a small net effect of drawing from public transit, but this
effect was subject to a few very important nuances. The first was that the slight reduction in public
transit use was not universal across all organizations. For most organizations, the overall net change in
rail or bus use was not statistically different from zero. A minority of the organizations (3 of 11)
exhibited a slight, but statistically significant shift away from public transit; this was large enough to
make the overall shift statistically significant. This impact was likely driven by two main factors. One
factor was that a majority of people that join carsharing were carless; hence, carsharing provided
additional vehicle access, which comes at the expense of other modes previously used, including
public transit. Evidence from Martin and Shaheen indicated that this increase in personal driving was
small individually, but it was enough to displace some public transit use [1]. The drop in public transit
usage was countered by a similarly sized increase of other members who joined carsharing and
reduced their vehicle holdings and their driving. In contrast, modal shares of walking, biking, and
carpooling all increased within the entire sample. When these impacts were combined across modes,
Energies 2011, 4
2111
more people increased their public transit and non-motorized modes use after joining carsharing
than decreased.
The instruments the authors employed in this study permitted a unique analysis of the commute
patterns of carsharing members. The estimations of travel by walking, bicycling, public transit, and
auto populated key characteristics of carsharing member commutes. The results of this analysis
showed that carsharing members generally have had short commutes to work relative to people living
in the same neighborhood. Nearly 85% of all carsharing members in the sample lived within
10 kilometers of their workplace. Furthermore, when divided into four regional subgroups in the U.S.
and Canada, the distribution of distance to work was found to be remarkably similar in shape and
spread. The comparison of distance to work of carsharing users to the general population within the
same neighborhoods suggested that even for urban regions, carsharing members exhibited shorter than
average commutes. Finally, zip and postal code information were used to link respondents to
population density information for their home location. This permitted the authors to conduct an
analysis of the change in annual VKT as categorized by regional population density. The analysis
suggested that population densities under 10,000 people per square kilometer have had similar average
changes in VKT. While the VKT change observed at higher densities also appears negative, increased
variability is present due to the dispersion of the sample throughout North America.
The results of this study have several implications for transportation and energy managers. Overall,
carsharing systems offer an energy-friendly transportation alternative that results in reduced driving
and vehicle ownership. This reduction was driven by a minority of members (~30% as reported in [1])
that made a shift to a new travel lifestyle, which was characterized by increased walking, bicycling,
carpooling, and public transit use. Planners seeking to support carsharing as a means to facilitate
energy use reductions should understand that the availability of infrastructure supporting these modes
is needed for carsharing to be successful. Most notably, the shift towards increased walking and
bicycling was dominant within the carsharing population. This shift, coupled with the relatively short
commute distances of carsharing members, defines an environment in this study in which carsharing
may have been likely to be relatively more successful in reducing energy use and emissions. For
planners, this implies that investments in pedestrian and bicycling infrastructure can serve to support
an environment in which carsharing becomes more viable for people who otherwise rely more heavily
on their personal vehicles. The authors’ evaluation of density as correlated to average VKT reductions
offered encouraging insights suggesting that carsharing can successfully reduce driving in low-density
urban environments. It was in fact very high-density urban environments where the reductions in
driving were less certain. Thus, although carsharing has gained prominence through growth in large
cities, the basic ingredients for success exist in smaller cities with supportive infrastructure for
alternative transportation. This has been evidenced by carsharing organizations that have thrived over
the past decade in smaller cities, such as Madison, Wisconsin and Victoria, British Columbia.
In terms of overall use, bus and rail transit modes are not increased by carsharing in this study.
Instead, the public transit customer base changes. Those carless households gaining mobility may step
onto public transit less, and those households reducing car dependency may step onto it more. Such
results are reflective of the first decade of carsharing in which carsharing growth absorbed large
numbers of carless households gaining a new mobility option.
Energies 2011, 4
2112
The results of the three North American carsharing survey evaluations have revealed a number of
insights providing a detailed look at how carsharing impacted GHG emissions, vehicle holdings, and
modal shift. Within each evaluation, subtleties and nuances of carsharing’s impact were observed.
Martin and Shaheen found that carsharing has reduced GHG emissions, but that a majority of members
actually increased their emissions by small increments [1]. These were offset by other members
decreasing their emissions by more substantial quantities, resulting in an average reduction in
emissions [1]. Martin et al. looked specifically at the change in vehicle holdings of carsharing
members [2]. The authors found that carsharing reduced average vehicle holdings by a degree that was
statistically significant, in spite of the fact that the majority of households joining carsharing were
carless when they joined. This study built on these results by exploring the impact that carsharing has
had on the travel patterns of members. Similarly, the authors found an impact that is complex. Some
members increased public transit, while others shifted away, and the magnitude of those shifts were
variable across organizations. At the same time, a reduction in auto commuting, as well as an increase
in walking, biking, and carpooling were observed to be statistically significant.
It is possible that these dynamics may change over time. If carsharing continues to attract a larger
membership base, it is probable that they will consist of a greater share of car-holding households in
the future. Evidence presented from these data suggests that such households are more likely to shift
towards public transit. All three studies suggest that carsharing members reduce their automobile use
overall and some through commuting. If carsharing continues to thrive and attract a greater share of
car-owning households, then greater shifts towards public transit could emerge. Given these
uncertainties, further study of the growth and development of carsharing is warranted to understand its
contributions to reductions in transportation energy use.
Acknowledgments
The Mineta Transportation Institute and the California Department of Transportation generously
funded this research. The authors would like to thank the carsharing programs in North America that
participated in the survey. Thanks also goes to Caroline Rodier, Adam Cohen, Denise Allen, Melissa
Chung, Kathleen Wong, Stephen Chua, Brenda Dix, Elaine Mao, Seth Contreras, and Meera Velu of
the Transportation Sustainability Research Center and the Innovative Mobility Research group at the
University of California, Berkeley for their assistance with the literature review and survey
development. The authors would also like to Neil Weiss of Arizona State University, as well as Asim
Zia and Alexander Gershenson of San Jose State University. In addition, gratitude goes to Dave Brook,
Clayton Lane, and Kevin McLaughlin for their assistance with survey development and review. The
contents of this paper reflect the views of the authors and do not necessarily indicate acceptance by
the sponsors.
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